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A summary of the first 6 months of the Open PHACTS IMI project, presented by Richard Kidd to the Healthcare Innovation Seminar 2011.
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An infrastructure project
Develop / apply a set of robust standards…
Implementing the standards in a semantic integration platform (“Open Pharmacological Space”)…
Delivering services to support on-going drug discovery programs in pharma and public domain
Mix ideal with the pragmatic. Build open that can accommodate non-open components in the real world.
Major work streamsBuild: OPS service layer and resource integration “commons”
Drive: Development of exemplars & applications
Sustain: Community engagement and long-term sustainability
OPS Services• Integrate data on target expression, biological
pathways and pharmacology to identify the most productive points for therapeutic intervention
• Investigate the in vitro pharmacology and mode-of-action of novel targets to help develop screening assays for drug discovery
• Compare molecular interaction profiles to assess potential off-target effects and safety pharmacology
• Analyse chemical motifs against biological effects to deconvolute high content biology assays
Guiding principle is open access, open usage, open source
- Key to standards adoption -
Assertion & Meta Data MgmtTransform / TranslateIntegrator
OPS Service Layer
Corpus 1
‘Consumer’Firewall
SupplierFirewall
Db 2
Db 3
Db 4
Corpus 5
Std PublicVocabularies
TargetDossier
CompoundDossier
PharmacologicalNetworks
BusinessRules
Work Stream 1: Open Pharmacological Space (OPS) Service LayerStandardised software layer to allow public DD resource integration− Define standards and construct OPS service layer− Develop interface (API) for data access, integration
and analysis− Develop secure access models
Existing Drug Discovery (DD) Resource Integration
Work Stream 2: Exemplar Drug Discovery Informatics toolsDevelop exemplar services to test OPS Service Layer Target Dossier (Data Integration)Pharmacological Network Navigator (Data Visualisation)Compound Dossier (Data Analysis)
Open flavoursOPS Open - open access to allOPS Consortia - data sets licensed just to the consortia OPS Academia - fully open to academia. “My OPS”
Open Source Open Access Infrastructure. GUI and back-end platform, online at openphacts.org or download both + data for local setup. Open Services: for example, RSC services. Open Data + Private Data: licensing fun for all the family.Commercial providers: abstract service interface to swap in commercial and open source platforms
Developers(Builders)
End users(Drivers)
A use case driven approach
Prioritised research questions
Benchmarkpilots
Target validation work-bench: in silico
target validation studies
Fusion/aggregation of data from different
domains to improve predictions of drug-
transporter interactions
Combination of physicochemical data & data from
transporter interaction for
prediction of blood-brain barrier
permeation and tissue distribution
Prioritised data sources
N
N NH2
NH2
OMe
MeO
MeO
Developers(Builders)
End users(Drivers)
A use case driven approach
Prioritised research questions
Target dossiers about targets, incorporating related
information on sequences, structures, pathways, diseases and small
molecules
Chem/bio space navigator of sets of pharmacologically annotated small
molecules, by chemical substructures, pharmacophores,
biological activities
Polypharmacology browser map coverage of the chemo-biological space for polypharmacological profiling of small molecules
Prioritised data sources
Exemplars
Example research questions
• Give all compounds with IC50 < xxx for target Y in species W and Z plus assay data
• What substructures are associated with readout X (target, pathway, disease, …)
• Give all experimental and clinical data for compound X• Give all targets for compound X or a compound with a
similarity > y%
73 questions identified across consortium
Required cheminformatics functionality
Chemical substructure searching
Chemical similarity searching
Required bioinformatics functionality
Sequence and similarity searching
Bioprofile similarity searching
BiologyEntrezGeneHGNCUniprotInterproSCOPWikipathwaysOMIMIUPHAR
Selection of prioritised data sources
ChemistryChEMBLDrugBankChEBIPubChemChemSpiderHuman Metabolome DBWombat (commercial)OntologiesAmiGo (The Gene Ontology)KEGG (Kyoto Encyclopedia of Genes and Genomes)OBI (The Ontology for Biomedical Investigations)Bioassay Ontology EFO (Experimental Factor Ontology)
Prioritised research questions analysisPrevalent Concepts
CompoundBioassayTargetPathwayDiseasePrevalent data relationshipsCompound – targetCompound – bioassayBioassay – targetCompound – target – mode of actionTarget – target classificationTarget – pathwayTarget – diseasePathway – disease
• Produce a working “lash up” system • Constrained to technologies in consortium + a few
data sources• Focused on 2 prioritized research questions (Q15 and
Q30)• Q 15: All oxidoreductase inhibitors active
<100nM in both human and mouse• Q 30: For a given compound [clozapine], give
me the interaction profile with [human or mouse] targets
• Minimum requirements: two data sources (one targets, one compounds) and able to produce answers in “manual time”.
• Brenda, KEGG, PDSP, ChEMBL, ChEBI, ENZYME DB, Chem2Bio2RDF
Agile development: 6 month “lash up”
Uri mapping
ChEMBL ScaiView Index
ConceptWiki
ConceptWikiUI
BridgeDB
WikiPathways
PathPhysio
UtopiaDocs
LinkedLifeData
ConceptWiki
ChemSpider
LarKC
LinkedOpen
DrugData
Other mappings
Term mapping
User Interfacesoftware
Identity ResolutionService
Distributed system
LarKC in the core
SPARQL or LarKC plugin
IRS to disambiguate
Data sources
SPARQL
Outcomes of exercise:
• Team building• Performance /
scalability analysis
• Does it provide an adequate answer to the questions 15 and 30?
• Demo for users (drive group) to recalibrate build tasks in order to better respond to user requirements
Build a lash up
rdf mapping
id mapping
concept mapping
interface
data sources
triple store
chemical resolution
Chem2Bio2RDF
text mining
LSP4All (Lundbeck) Generic Interface search by enzyme familyQ15: All oxidoreductase inhibitors active <100nMolars in both human & mouse
Pharmacological dataExact and structure search
Navigate from compounds to targets
UTOPIA Documents (U Manchester)
PathVisio (Maastricht U)Biological dataGenes suggestion for selected protein
“Lash Up” Sanity CheckQ15: All oxidoreductase inhibitors active <100nM in both human and mouse
• IC50 values and compounds fully coincident between the automatic and manual search.
• “Lash up” identified a compound lost in the manual search (Raloxifene) which value after doing a new manual search was correct.
• Manual search took 3 days (Mabel Loza’s team @USC)
• Automated search took milliseconds (OWLlm).
Demo: www.youtube.com/openPHACTS
Prototype Architecture
Onwards and Upwards
Connection between developers and usersSolidify interfaces for exemplar developersReview lash up for technology, content and exemplars
ArchitectureServices: e.g. entity identification and resolution and representing similarity, ORCID, DataCite
Models: RDF / Nanopublication model spec and guidelines
Tender documents for commercial storage providers
PrototypeMarch 2012: Internal Prototype DeliverySeptember 2012: Release 1st Prototype
Focus on different aspects of drug discovery, the technology used, data sharing, sustainability, licensing and practical applications.
1st Volendam (near Amsterdam) September 19-20, 2011 Joint with GEN2PHEN Solving Bottlenecks in Data Sharing in the Life Sciences
2nd Location TBD April 16-17, 2012
OPS Community Workshops
SummaryRobust standards and techniques
Solid integration between data sources via semantic technologies Development of high quality assertionsWorkflows and analysis pipelines across resources
A semantic integration hub (“Open Pharmacological Space”)Open, public domain infrastructure for drug discovery data integrationOpen web-services for drug discoverySecure access model to enable queries with proprietary data (pharma, SME, NGO and PPP)
Deliver services To support on-going drug discovery programs in pharma and public domainAlign development of standards, vocabs and data integration to selected drug discovery issues
Developers(Builders)
End users(Drivers)
Academia-Commercial Venture
FocusOne area - pharmacology“Production Level” softwareCurrency/Updates & Licensing keySemantic Pragmatics: everyday use by scientists not informaticians
FutureAn infrastructure that can be built upon, to provide a stable foundation for further pre-competitive informatics collaborationSustainability
The Open PHACTS project is funded by the IMI Programme.The Innovative Medicines Initiative (IMI) is a uniquepublic-private partnership designed by the EuropeanCommission and European Federation of PharmaceuticalIndustries and Associations (EFPIA). It is a pan-European collaboration that brings together largebiopharmaceutical companies, small- and medium sizedenterprises (SMEs), patient organisations,academia, hospitals and public authorities.
Starting date: 01/03/2011Duration: 36 monthsIMI funding: € 9.988.867Other contributions: € 2.265.938EFPIA in kind contribution: € 4.142.649Total project cost: € 16.397.454
CONTACTS
Project Coordinator: Bryn Williams-JonesPfizer / Connected DiscoveryEmail: [email protected]
Managing entity of IMI beneficiariesProf Gerhard EckerProfessor of PharmacoinformaticsDepartment of Medicinal ChemistryUniversity of Wien, AustriaEmail: [email protected]
EFPIA MEMBER COMPANIES• AstraZeneca AB, Sweden• Eli Lilly and Company Ltd, UK• GlaxoSmithKline Research & Development Ltd, UK• H. Lundbeck A/S, Denmark• Laboratorios del Dr. Esteve S.A, Spain• Merck, Germany• Novartis Pharma, AG, Switzerland• Pfizer Ltd, UK
UNIVERSITIES, RESEARCH ORGANISATIONS, PUBLIC BODIES & NON-PROFIT• Barcelona Mar Parc Health Consortium, Spain• Christian Association for Higher Education,Research and Patient Care, Netherlands• Leiden University Medical Centre(LUMC), Netherlands• Maastricht University, Netherlands• National Centre for Cancer Research (CNIO), Spain• Rheinische Friedrich-Wilhelms-Universität Bonn, Germany• Royal Society of Chemistry, UK• Technical University of Denmark, Denmark• University of Hamburg, Germany• University of Manchester, UK• University of Santiago de Compostela, Spain• University of Wien, Austria
SMEs• Academic Concept Knowledge Limited, UK• BioSolveIT GmbH, Germany
Project communication
www.openphacts.org
@open_phacts